Research on Occluded Objects 6DoF Pose Estimation with Multi-feature and Pixel- level Fusion

Abstract

In order to solve the problem that current robots are difficult to achieve accurate 6DoF pose estimation under the environment of occluded objects and insufficient lighting, in this paper, a pixel-level based neural network framework is proposed, which includes three modules, the RGB feature extraction networks module, the pixel-level fusion module and the 6D pose regression network module. The RGB feature extraction networks module firstly segments the target objects and then extracts the objects?? features. The pixel-level fusion module is applied for fusing RGB features with 3D multi-view features. And the last module fuses 3D point cloud pixels and outputs the 6D pose of the objects. The experiments conducted on the YCB-Video dataset, the LINEMOD dataset, and the YCB-Occlusion dataset processed in this paper manifest that the framework proposed can effectively predict the 6D pose of the objects even when the objects are occluded or the point clouds of the object are lost. Furthermore, compared with other frameworks, this framework is more robust and the efficiency is improved by hundreds of times only with a small loss of accuracy.

Keywords

• pixel-level fusion
• convolutional neural networks (cnn)
• point cloud feature fusion
• 6dof pose estimation